15 results about "Service placement" patented technology

The present invention discloses a service placement and task offloading strategy method and system for edge environments based on computational reuse. This method addresses the quality of service (QoS) issues associated with computing services that are frequently requested by users in resource-constrained edge environments. It divides the service request processing model into three parts: one part is processed through computational reuse, another part is processed by edge servers, and the remaining part is handled by cloud servers. A reasonable service placement model is also established, enabling more requests to be served within limited edge resources, thereby fully utilizing edge resources and improving overall service quality.

The invention discloses a combined service placement, task scheduling and resource allocation method in a multi-sensor multi-user edge computing network. The method comprises the following steps: constructing a multi-sensor multi-user edge computing system model; establishing a user task processing and sensor data collection delay and energy consumption model, and further establishing a system overhead optimization problem; an optimization problem is decomposed, and a user and sensor transmitting power allocation decision and a computing resource and transmission rate allocation decision are solved based on a numerical optimization algorithm; a service placement and task scheduling sub-problem is constructed into a dual-time-scale Markov decision process, and a combined service placement, task scheduling and resource allocation strategy is trained and applied based on a deep reinforcement learning technology. According to the method, factors such as the user task arrival rate, the sensor data size and the edge server resources are comprehensively considered, service placement, task scheduling and resource allocation decision are jointly optimized, efficient operation of the edge system is guaranteed, and user experience is improved.

This disclosure describes techniques and mechanisms for utilizing affinity routing in SDWAN networks. The techniques may enable network administrators to assign and / or configure affinity numbers to hub(s) and / or gateway(s), tunneling interface(s), service(s), etc., as well as affinity-preference-order(s) to edge device(s) within the network. Network administrators may also configure control polic(ies). The techniques enable a scalable and simplified way to automatically load-balance traffic across different gateways within a network, while reducing network resource usage. The techniques may utilize routing affinity to achieve a variety of networking related functionalities, including automatic load-balancing of traffic, provisioning of active and backup gateways, optimal route distribution to routers from routing controllers, optimized service placement for edge routers, without the need for any policy configuration at all, let alone complex policies.

A method for an energy-efficient service placement in a mobile edge cloud comprising at least one edge site is disclosed. The method comprises receiving a service placement request from a service provider. The method comprises identifying a set of candidate edge site groups and calculating a first energy efficiency value for each identified candidate edge site group. Further, the method comprises calculating a second energy efficiency value for components of the cellular network that are involved in the communication between the user device and the edge site. The method comprises determining for each candidate edge site group, an energy efficiency metric for deploying said service placement request in a traffic path of the cellular network based on the first and second energy efficiency values. The method further comprises determining a service placement policy for the service placement based on the calculated energy efficiency metric and the obtained performance parameters.

Task unloading and service placement are two key technologies for reducing task completion time delay in the field of edge computing research, however, task unloading decisions and service placement decisions are optimized separately in existing research, the problem is simplified to a certain degree through the method, but the close relation and mutual influence between the task unloading decisions and the service placement decisions are neglected. In order to deal with the challenge, the invention provides a joint optimization scheme, a hierarchical deep reinforcement learning (HDQN) architecture is provided aiming at dual constraints of task dependence and service deployment in a complex scene, a joint optimization problem is decoupled into two decision levels, an upper layer carries out resource allocation through service placement optimization, and a lower layer carries out resource allocation through service deployment optimization. And the lower layer performs real-time task scheduling based on a dynamic environment. Through the layering mechanism, task unloading and resource constraint are effectively coordinated, and the dynamic change of the edge computing environment is adaptively coped with. Experimental results show that the method has excellent performance in the aspect of reducing task completion time.

This invention discloses a method for a network architecture for service placement and caching in a mobile edge computing system, relating to the fields of digital twins, network architecture, and optimization algorithms. Based on a Merkle Tree (a hash-value-based tree data structure) and an improved genetic algorithm, this method achieves a trade-off between computational complexity and accuracy compared to traditional service placement algorithms. By combining digital twin network technology, wireless communication technology, and specific network communication scenarios in the context of 6G, this method minimizes cloud load while satisfying user constraints, improving the efficiency of user service type requests.

A method for an energy-efficient service placement in a mobile edge cloud comprising at least one edge site is disclosed. The method comprises receiving a service placement request from a service provider. The method comprises identifying a set of candidate edge site groups and calculating a first energy efficiency value for each identified candidate edge site group. Further, the method comprises calculating a second energy efficiency value for components of the cellular network that are involved in the communication between the user device and the edge site. The method comprises determining for each candidate edge site group, an energy efficiency metric for deploying said service placement request in a traffic path of the cellular network based on the first and second energy efficiency values. The method further comprises determining a service placement policy for the service placement based on the calculated energy efficiency metric and the obtained performance parameters.

System and method embodiments of centralized discovery controller (CDC) services on a switching network fabric are disclosed. Nodes on the switching network fabric are categorized into leader nodes, e.g., spine switches, and follower nodes, e.g., leaf switches. The CDC services may be divided into centralized CDC services and distributed CDC services, which have direct connections to endpoints for seamless horizontal scaling of endpoints. The centralized CDC services may be placed on a leader node, and the distributed CDC services may be placed among the follower nodes. Operations of the CDC services may be monitored. In case of a node failure, CDC services deployed on the failed node may be moved or transferred to one or more other nodes that have the same role as the failed node. Implementations of the CDC service placement embodiments enable seamless horizontal scaling for endpoints and provide capability for more visibility and monitoring of endpoint.

The invention relates to a service placement and task unloading method based on a tree genetic programming hyper-heuristic algorithm in a cloud-edge collaborative environment, and belongs to the technical field of task unloading of cloud computing and edge computing. According to the method, the service placement and task unloading strategy can be dynamically generated according to the resource configuration and the input task, so that the task processing reliability is improved, and the delay is reduced. The method comprises the following steps: firstly, designing a group of low-level heuristic algorithms according to the characteristics of a cloud edge collaborative environment and a task unloading target; then, the low-level heuristic algorithm is used as a gene to construct individual coding trees, and each tree represents a heuristic algorithm generated by a dynamic selection and combination low-level heuristic algorithm so as to initialize a population; secondly, iteratively evolving the population through selection, crossover and mutation operations, and reserving excellent individuals in the population evolution process by adopting an elitism strategy; finally, a heuristic algorithm with good performance in the aspects of reliability and delay optimization is generated and used for guiding service placement and task unloading.

The invention provides a task migration and service placement method and system based on an edge computing architecture, and the method comprises the steps: carrying out the modeling of communication channels of all devices and an edge layer, calculating channel parameters based on a modeled model, considering the total cost of all devices based on the calculated parameters, and formulating a joint resource optimization problem of task migration and service placement; a joint resource optimization problem is converted into a Markov decision process model, state spaces of intelligent agents are designed, a service placement allocation decision, a computing resource allocation decision, a computing resource allocation proportion and a wireless channel bandwidth proportion allocation decision are used as action spaces of each intelligent agent, and based on the goal of the joint resource optimization problem, the service allocation decision and the computing resource allocation proportion and the wireless channel bandwidth proportion allocation decision are calculated. Minimizing the total cost of each device as a reward function, and iteratively performing optimization solution by using a task migration and service placement optimization algorithm on the edge node to obtain a final task migration and service placement strategy. According to the invention, the robustness of the system can be improved while the total cost of task processing of the system is reduced.

The present invention discloses a task-aware service placement method for distributed learning in a wireless edge network. The present invention places services for distributed learning applications based on a service directed acyclic graph with resource and communication demand information obtained by merging the original task directed acyclic graph. This step includes the following two stages: the first stage is a stage of searching for server nodes to be placed for each independent service directed acyclic graph after merging; the second stage is a stage of evaluating the overall delay of a given service placement scheme; by iterating the above two steps, the optimal distributed learning application service placement scheme is found. In response to the problems of unreliable communication, random user movement, and service queuing, the present invention proposes a task-aware service placement method that takes into account the dependencies between placed services, thereby reducing the expected delay of all learning tasks and improving the task completion rate of the edge network. The present invention is applicable to the field of edge computing.

Methods, systems, and computer program products for cold-start service placement over on-demand resources are provided herein. A computer-implemented method includes obtaining a performance requirement profile comprising performance requirements of a service that vary over time; determining a plurality of incarnations for the service, wherein each incarnation is associated with a level of performance provided by the incarnation for the service, resource requirements of the incarnation, and a type of computing node the incarnation is configured to execute on; identifying computing nodes having different types and different resource capacities; jointly scheduling (i) the computing nodes and (ii) one or more of the incarnations on the computing nodes over a time interval such that a cumulative level of performance of the incarnations scheduled at each timepoint in the time interval satisfies the performance requirement profile of the service.

The invention belongs to the technical field of wireless communication, and discloses a marginal air-ground network optimization method and system based on hierarchical deep reinforcement learning (joint optimization method and system), and the optimization of the marginal air-ground network is realized through joint optimization of a service placement strategy, UAV trajectory planning, access control, bandwidth allocation and transmission power. And efficient equipment connection and data processing services are provided for disaster area rescue, urban intelligent monitoring and other scenes. According to the method, a dual-time-scale optimization strategy is adopted: on a coarse-grained frame-level time scale, a high-level agent adopts a deep Q network (DQN) proxy, and a placement strategy of service in an MEC server is decided according to a UAV task request to ensure that resource constraints are met; on the fine-grained time slot level time scale, a low-layer agent adopts an improved depth deterministic policy gradient (IDDPG) for proxy, the UAV trajectory, TDMA-based access control, bandwidth allocation proportion and transmission power are optimized, and the real-time task requirement is met. Through the layered framework, the system can improve the task success rate, the energy efficiency and the resource scheduling fairness in a dynamic environment, and efficient and stable air-ground integrated network service is realized.

This disclosure describes techniques and mechanisms for utilizing affinity routing in SDWAN networks. The techniques may enable network administrators to assign and / or configure affinity numbers to hub(s) and / or gateway(s), tunneling interface(s), service(s), etc., as well as affinity-preference-order(s) to edge device(s) within the network. Network administrators may also configure control polic(ies). The techniques enable a scalable and simplified way to automatically load-balance traffic across different gateways within a network, while reducing network resource usage. The techniques may utilize routing affinity to achieve a variety of networking related functionalities, including automatic load-balancing of traffic, provisioning of active and backup gateways, optimal route distribution to routers from routing controllers, optimized service placement for edge routers, without the need for any policy configuration at all, let alone complex policies.

The present application provides a method and apparatus for dynamic expansion and placement of edge computing services, the method comprising: automatically expanding the number of microservice replicas for target data processing requests based on the current workload intensity prediction results of each microservice corresponding to each application in the edge computing platform and the current work performance evaluation results of each edge node, so as to determine the scaled optimized number of microservice replicas; using a preset adaptive discrete binary particle swarm optimization algorithm, according to the scaled optimized number of microservice replicas, the current number of placeable edge nodes, and performance information, obtaining the mapping relationship between each microservice replica and each available edge node, so as to place each microservice replica to the corresponding edge node. The present application can improve the reliability and effectiveness of automatic expansion of edge computing services in edge environments where edge load is unbalanced and network status is unreliable, and improve the accuracy and reliability of edge computing service placement.